The method of multivariate regression by Partial Least Squares (PLS) was used to construct a calibration model of the LIBS spectra for prediction of total carbon in the samples. This method was used because it reduces the dimension of the input variables and maximizing the correlation with the response variable [1]}, [2]}. With the established prediction model, was made the validation of the results obtained with those of the reference method, which gives the Pearson coefficients that measure the degree of correlation between two variables. They are represented by \(\rho \)  and assumes values between \(-1\)  and 1, where \(\rho \ge 0.7\)  indicates strong correlation, \(0.3<\rho <0.7\)  indicates moderate correlation and \(\rho \le 0.3\)  corresponds to a weak correlation between the results [3]}. The most used value to quantify differences between estimated values by the model and the reference measures is the root mean square uncertainty (RMSE) of the correlation, which can be estimated by the equation::
\(RMSE=\sqrt{\frac{1}{n}\sum \limits _{i=1}^{n}{(y_{i}-\hat{y}_i)^{2}}},\) 
